Phase-type survival trees and mixed distribution survival trees for clustering patients' hospital length of stay

Clinical investigators, health professionals and managers are often interested in developing criteria for clustering patients into clinically meaningful groups according to their expected length of stay. In this paper, we propose two novel types of survival trees; phase-type survival trees and mixed distribution survival trees, which extend previous work on exponential survival trees. The trees are used to cluster the patients with respect to length of stay where partitioning is based on covariates such as gender, age at the time of admission and primary diagnosis code. Likelihood ratio tests are used to determine optimal partitions. The approach is illustrated using nationwide data available from the English Hospital Episode Statistics (HES) database on stroke-related patients, aged 65 years and over, who were discharged from English hospitals over a 1-year period.peer-reviewe

The extended mixture distribution survival tree based analysis for clustering and patient pathway prognostication in a stroke care unit

In our previous work we proposed a special class of survival distribution called Mixture distribution survival trees, which are constructed by approximating different nodes in the tree by distinct types of mixture distributions to achieve more improvement in the likelihood function and thus the improved within node homogeneity. We proposed its applications in modelling hospital length of stay and clustering patients into clinically meaningful patient groups, where partitioning was based on covariates representing patient characteristics such as gender, age at the time of admission, and primary diagnosis code. This paper proposes extended Mixture distribution survival trees and demonstrates its applications to patient pathway prognostication and to examine the relationship between hospital length of stay and/or treatment outcome. 5 year retrospective data of patients admitted to Belfast City Hospital with a diagnosis of stroke is used to illustrate the approach.peer-reviewe

Phase-Type Survival Trees to Model a Delayed Discharge and Its Effect in a Stroke Care Unit

The problem of hospital patients’ delayed discharge or ‘bed blocking’ has long been a challenge for healthcare managers and policymakers. It negatively affects the hospital performance metrics and has other severe consequences for the healthcare system, such as affecting patients’ health. In our previous work, we proposed the phase-type survival tree (PHTST)-based analysis to cluster patients into clinically meaningful patient groups and an extension of this approach to examine the relationship between the length of stay in hospitals and the destination on discharge. This paper describes how PHTST-based clustering can be used for modelling delayed discharge and its effects in a stroke care unit, especially the extra beds required, additional cost, and bed blocking. The PHTST length of stay distribution of each group of patients (each PHTST node) is modelled separately as a finite state continuous-time Markov chain using Coxian-phase-type distributions. Delayed discharge patients waiting for discharge are modelled as the Markov chain, called the ‘blocking state’ in a special state. We can use the model to recognise the association between demographic factors and discharge delays and their effects and identify groups of patients who require attention to resolve the most common delays and prevent them from happening again. The approach is illustrated using five years of retrospective data of patients admitted to the Belfast City Hospital with a stroke diagnosis

Entrapment of Autologous von Willebrand Factor on Polystyrene/Poly(methyl methacrylate) Demixed Surfaces

Human platelets play a vital role in haemostasis, pathological bleeding and thrombosis. The haemostatic mechanism is concerned with the control of bleeding from injured blood vessels, whereby platelets interact with the damaged inner vessel wall to form a clot (thrombus) at the site of injury. This adhesion of platelets and their subsequent aggregation is dependent on the presence of the blood protein von Willebrand Factor (vWF). It is proposed here that the entrapment of vWF on a substrate surface offers the opportunity to assess an individual’s platelet function in a clinical diagnostic context. Spin coating from demixed solutions of polystyrene (PS) and poly(methyl methacrylate) (PMMA) onto glass slides has been shown previously to support platelet adhesion but the mechanism by which this interaction occurs, including the role of vWF, is not fully understood. In this work, we report a study of the interaction of platelets in whole blood with surfaces produced by spin coating from a solution of a weight/weight mixture of a 25% PS and 75% PMMA (25PS/75PMMA) in chloroform in the context of the properties required for their use as a Dynamic Platelet Function Assay (DPFA) substrate. Atomic Force Microscopy (AFM) indicates the presence of topographical features on the polymer demixed surfaces in the sub-micron to nanometer range. X-ray Photoelectron Spectroscopy (XPS) analysis confirms that the uppermost surface chemistry of the coatings is solely that of PMMA. The deliberate addition of various amounts of 50 μm diameter PS microspheres to the 25PS/75PMMA system has been shown to maintain the PMMA chemistry, but to significantly change the surface topography and to subsequently effect the scale of the resultant platelet interactions. By blocking specific platelet binding sites, it has been shown that their interaction with these surfaces is a consequence of the entrapment and build-up of vWF from the same whole blood sample

An extended phase type survival tree for patient pathway prognostication

Survival tree based analysis is a powerful method of prognostication and determining clinically meaningful patient groups from a given dataset of patients' length of stay. In our previous work [1, 2] we proposed a phase type survival tree method for clustering patients into homogeneous groups with respect to their length of stay where partitioning is based on covariates representing patient characteristics such as gender, age at the time of admission, and primary diagnosis code. This paper extends this approach to examine the relationship between LOS in hospital and destination on discharge among these patient groups. An application of this approach is illustrated using 5 year retrospective data of patients admitted to Belfast City Hospital with a diagnosis of stroke (hemorrhagic stroke, cerebral infarction, transient ischaemic attack TIA, and stroke unspecified).peer-reviewe

Nanoindentation and nano-scratching of hydroxyapatite coatings for resorbable magnesium alloy bone implant applications

The corrosion rate of Mg alloys is currently too high for viable resorbable implant applications. One possible solution is to coat the alloy with a hydroxyapatite (HA) layer to slow the corrosion and promote bone growth. As such coatings can be under severe stresses during implant insertion, we present a nano-mechanical and nano-tribological investigation of RF-sputtered HA films on AZ31 Mg alloy substrates. EDX and XRD analysis indicate that as-deposited coatings are amorphous and Ca-deficient whereas rapid thermal annealing results in c-axis orientation and near-stoichiometric composition. Analysis of the nanoindentation data using a thin film model shows that annealing increases the coating's intrinsic hardness (H) and strain at break (H/E) values, from 2.7 GPa to 9.4 GPa and from 0.043 to 0.079, respectively. In addition, despite being rougher, the annealed samples display better wear resistance; a sign that the rapid thermal annealing does not compromise their interfacial strength and that these systems have potential for resorbable bone implant applications

A phase type survival tree model for clustering patients’ hospital length of stay

Clinical investigators, health professionals and managers are often interested in developing criteria for clustering patients into clinically meaningful groups according to their expected length of stay. In this paper, we propose phase-type survival trees which extend previous work on exponential survival trees. The trees are used to cluster the patients with respect to length of stay where partitioning is based on covariates such as gender, age at the time of admission and primary diagnosis code. Likelihood ratio tests are used to determine optimal partitions. The approach is illustrated using nationwide data available from the English Hospital Episode Statistics (HES) database on stroke-related patients, aged 65 years and over, who were discharged from English hospitals over a 1-year period.peer-reviewe